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Generalization Bounds

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TitleStatusHype
On the Importance of Gradient Norm in PAC-Bayesian Bounds—0
On the Role of Generalization in Transferability of Adversarial Examples—0
On the sample complexity of parameter estimation in logistic regression with normal design—0
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond—0
On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond—0
Optimal deep learning of holomorphic operators between Banach spaces—0
Optimal Weak to Strong Learning—0
Optimization and Adaptive Generalization of Three layer Neural Networks—0
Optimizing Information-theoretical Generalization Bounds via Anisotropic Noise in SGLD—0
Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD—0
Out-of-distribution generalization for learning quantum dynamics—0
PAC-Bayes and Domain Adaptation—0
PAC-Bayes and Information Complexity—0
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction—0
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning—0
PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons—0
PAC-Bayesian Neural Network Bounds—0
PAC-Bayesian Offline Contextual Bandits With Guarantees—0
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization—0
PAC-Bayesian Treatment Allocation Under Budget Constraints—0
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes—0
Partial differential equation regularization for supervised machine learning—0
Light Unbalanced Optimal Transport—0
Passive Learning with Target Risk—0
A rescaling-invariant Lipschitz bound based on path-metrics for modern ReLU network parameterizations—0
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank—0
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach—0
PLUME: Polyhedral Learning Using Mixture of Experts—0
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks—0
Predictive Value Generalization Bounds—0
Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning—0
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds—0
Provable Generalization of Overparameterized Meta-learning Trained with SGD—0
Provable Pathways: Learning Multiple Tasks over Multiple Paths—0
Rademacher Complexity Bounds for Non-I.I.D. Processes—0
Rademacher Generalization Bounds for Classifier Chains—0
Random Maxout Features—0
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms—0
Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees—0
Recovery and Generalization in Over-Realized Dictionary Learning—0
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems—0
Regularized Gradient Boosting—0
Regularized Proportional Fairness Mechanism for Resource Allocation Without Money—0
Reinterpreting causal discovery as the task of predicting unobserved joint statistics—0
Relative Deviation Learning Bounds and Generalization with Unbounded Loss Functions—0
Relative Deviation Margin Bounds—0
Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression—0
Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics—0
Reward Generalization in RLHF: A Topological Perspective—0
Reweighted Mixup for Subpopulation Shift—0
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